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Deep Learning Empowered Structural Health Monitoring and Damage Diagnostics for Structures with Weldment via Decoding

Zi Zhang1, Hong Pan1, Xingyu Wang1

  • 1Department of Civil, Construction and Environmental Engineering, North Dakota State University, Fargo, ND 58018, USA.

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Summary
This summary is machine-generated.

This study uses deep learning, specifically convolutional neural networks (CNNs), to accurately detect and characterize welding defects in metallic structures. The AI method effectively identifies defect types, severity, and interactions, even in noisy conditions, ensuring structural integrity.

Keywords:
convolutional neural networkdata-driven approachmachine learningnondestructive detectionstructural health monitoringultrasonic guided wave

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Area of Science:

  • Materials Science and Engineering
  • Non-destructive Testing
  • Artificial Intelligence in Engineering

Background:

  • Welding is crucial for metallic structures like pipelines, but susceptible to defects (e.g., lack of penetration, undercut) that initiate cracking and corrosion.
  • Detecting welding defects and their interaction with other damages is vital for structural integrity, yet challenging due to weldment complexity and signal interpretation issues.
  • Current ultrasonic guided wave methods struggle with complex damage interactions and structural uncertainties, necessitating advanced data processing techniques.

Purpose of the Study:

  • To employ deep learning, specifically Convolutional Neural Networks (CNNs), for enhanced characterization of welding defects.
  • To accurately identify welding defect type, severity, location, and interactions with other damages using AI.
  • To validate the robustness of the deep learning approach against structural uncertainties and noise interference.

Main Methods:

  • Development and implementation of a Convolutional Neural Network (CNN) architecture for ultrasonic guided wave signal processing.
  • Design and training of the CNN model using 16 distinct damage states, including various welding defects and their interactions.
  • Testing the model's performance under conditions of structural uncertainties (different embedding materials) and high noise levels.

Main Results:

  • The deep learning method effectively and automatically extracts features from ultrasonic guided waves.
  • High-precision predictions for damage detection in complex situations involving welding defects were achieved.
  • The CNN model demonstrated robustness and high accuracy even with significant noise interference and structural uncertainties.

Conclusions:

  • Deep learning, particularly CNNs, offers a powerful and accurate solution for detecting and characterizing welding defects in metallic structures.
  • The proposed method significantly improves upon existing techniques by handling complex damage interactions and structural uncertainties.
  • The AI-driven approach ensures structural integrity by enabling early and reliable identification of potential failures in welded components.